Spectrum Signaling for Cognitive Underwater Acoustic Channel Allocation Dustin Torres, Zainul Charbiwala, Jonathan Friedman, and Mani Srivastava Networked and Embedded Systems Laboratory Electrical Engineering Department University of California, Los Angeles
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Abstract—The underwater environment varies significantly from traditional electromagnetics. Many cognitive radio techniques that have proven effective in the terrestrial setting can not simply be transplanted underwater. Furthermore the limited bandwidth and lack of FCC regulations additionally changes the approaches that can be taken. In this paper, we introduce spectrum signaling, an approach for distributed channel allocation for the underwater acoustic environment.
I. I NTRODUCTION Network communication underwater using sound waves is extremely difficult fundamentally due to the limited bandwidth and the limited propagation velocity of the medium. The compressive mechanical nature of the longitudinal acoustic wave, coupled with the great density of the water, results in higher transmission power for equivalent range and in limited bandwidth (extreme high frequency attenuation) [1]. Complicating matters further, this channel attenuation is strongly dependent on frequency resulting in a significant difference in channel quality across the usable spectrum (e.g. ∼1kHz to ∼100kHz [2]). The speed of sound underwater is approximately 1500 m/s – five orders of magnitude less than an airborne electromagnetic (EM) transmission [3] This slow velocity produces space and time-varying multipath leading to high inter-symbol interference and limited data rates. In spite of these issues, acoustics is the modality of choice for underwater communications, since traditional EM is severely attenuated in the conductive medium. It is, thus, essential to finesse the underwater acoustic network and maximize resource utilization to improve its performance. In order to take full advantage of the limited bandwidth available we propose a cognitive channel allocation scheme that is carried out in a distributed fashion. A link is established on demand between a pair of nodes, whenever a data transfer is required and when the transmission is complete, the channel can be reused by another pair wishing to communicate. In this way, there is no centralized node that is assigning channels but rather an appropriate channel is selected on a link-by-link basis. Since bandwidth is scarce in underwater acoustics, a MAC or channel allocation scheme should cause little or no bandwidth overhead. By using the proposed spectrum signaling and cognitive sensing techniques, we are able to achieve allocation with the only added bandwidth coming from an increase in the number of channels.
II. BACKGROUND In the terrestrial setting, cognitive radio has been of growing interest as the frequency spectrum becomes overcrowded in some bands while others remain underutilized [4]. Cognitive networks, by opportunistically accessing a portion of free spectrum, are able to increase performance and reduce collisions as compared to an overcrowded spectrum. This does not come for free. Government agencies worldwide regulate and delegate the use of airborne wireless spectrum. The licensed user (“primary user”) is given legal priority and should not be interfered with. In over the air RF frequencies, cognitive network nodes, which are referred to as secondary users must be able to detect the presence of a transmission from the primary user, and cease use of the band [5]. This process of looking for white space and primary users is known as spectrum sensing. Once the white space has been sensed, a cognitive network node may decide to opportunistically use this piece of the spectrum, which is referred to as spectrum access. A transmitter and receiver must agree on what frequency is going to be used before the data transmission begins. There have been many approaches on how this agreement between transmitter and receiver is made. One approach is the use of a control channel [4], wherein all parameters can be negotiated before the data transfer begins. Once the channel has been selected nodes will hop to the agreed channel and begin communication. While this approach may work for many terrestrial networks, this requires a dedicated channel – bandwidth overhead that is undesirable in the underwater setting. Furthermore, due to frequency dependent attenuation, the control channel has to be at a low enough frequency to accommodate the most distant link in the network. This band is precious for data transfers since it can travel the farthest and should not be expended for control exclusively [2]. Other approaches have included setting up multiple channels on the deployment of the cognitive network and if a primary user enters the space then to jump to a backup channel [6], [4]. However many of the requirements that must be followed by terrestrial radio do not apply to the underwater channel. For instance there is no user that must be avoided (e.g. primary user) in the underwater setting so extreme precautions do not need to be taken to avoid certain interference. This allows for
Fig. 1. Example spectrum signal shown in the frequency domain to call node 25 to a channel
justifiable liberties to be taken even at the cost of collisions. In the underwater environment spectrum sensing can take on a much different role. Since currently the rules that were established by the FCC about primary and secondary users do not exist, an underwater cognitive network can use the entire available spectrum. This does not mean that spectrum sensing is no longer needed, on the contrary it can be used to see which channels are occupied by neighboring links. In this way the spectrum is shared between many links in the same network rather then introducing additional networks. There has been previous work on how to efficiently use the limited bandwidth that is available underwater. For instance in [7] Peleato et el. propose a protocol to reuse the same frequency at a minimum distance based on the radius of a cluster of nodes and the attenuation through the water. This also utilizes the frequency dependent attenuation to ensure that given a frequency, radius of a cluster, and distance between clusters all interference is sufficiently attenuated, or the time it takes to travel the distance is great enough to avoid interference. However, this assumes a certain network architecture making it only useful for specific topologies. In [8], again the frequency dependent attenuation of underwater acoustics is looked at and a channel allocation strategy is proposed. Baldo et el. argue that given a topology and the distance information, the node that is closest to the transmitter should be assigned the higher frequency channel while the further away node the lower channel. This will increase attenuation on the shorter link, however total power transmitted is saved since the longer link has less attenuation. We try to build on this work by proposing a protocol that could make such an assignment if distance information is known. III. S PECTRUM S IGNALING P ROTOCOL The underwater channel has limited bandwidth for communication due to frequency dependent attenuation. Although this is a detriment for high data-rates, there is a distinct advantage that it offers over higher bandwidth terrestrial com-
Fig. 2. A sample topology with node 1 calling node 27 and node 2 calling node 14. The range of channel 1 is shown with a solid line and channel 3 with a dashed line.
munications. It is computationally feasible to take a snapshot of the complete underwater spectrum with a single Discrete Fourier Transform (DFT). The spectrum signaling protocol takes advantage of this fact that every receiving node can see every possible acoustic channel at any given time. The protocol works in a distributed fashion where any node can establish a channel for communication with all of its one hop neighbors, allowing the protocol to work with many network different topologies. A. Transmission and Spectrum Signaling If a node wants to transmit data then it will begin the process by taking a snapshot of the frequency domain with a Fast Fourier Transform (FFT). Looking at the predefined channels in the frequency domain, each channel can be marked as usable or not depending on the received power in a given channel. Upon picking the channel for transmission a node will send out a unique signal on the chosen channel. In a network with N nodes, if node i (Ni ) wants to send to node j (Nj ) then node i will use the binary value of Nj which can be thought of as a binary vector of length l. Based on the modulation scheme and maximum datarate, there will be a minimum bandwidth B needed for communication and a certain number of maximum channels C for a given communication range where total possible bandwidth is B · C. Channel i denoted Ci will have a minimum frequency fi,min and maximum frequency fi,max where B = fi,max − fi,min . To generate the signal needed to initiate communication the maximum length of any node ID binary vector is found by lmax = ⌈log2 (N )⌉. If l < lmax it can be padded with zeros until it reaches the maximum length. Once the appropriate channel has been chosen, the transmitter will then enter a spectrum signaling state to try and call the receiver to the appropriate channel. 1) Spectrum Signaling: A transmitting node will place the intended receivers node ID on the frequency spectrum in binary in the form of a sum of Dirac delta functions which can be thought of as a case of OFDM. If channel Ci is chosen
for communication then the signal in the positive frequency domain is: k=l max X 1 Nj,k · δ(f − fmin,i − ∆f (k − )) F (f ) = (1) 2 k=1
where
∆f = B/lmax
(2)
Here Nj,k is the kth element of the bit vector representing the node ID Nj . An example of this transmitted signal can be seen in figure 1 where the lowest frequency δ(f − f0 ) represents the most significant bit. In this example the channel chosen is 1 and B = 10 kHz, fmin,1 = 10 kHz, fmax,1 = 20 kHz, lmax = 5, ∆f = 2 kHz and receiving node j has node ID 25 making N25 = [1 1 0 0 1]. Therefore the signal to be sent in the frequency domain is: F (f ) = δ(f − 11 kHz) + δ(f − 13 kHz) + δ(f − 19 kHz) The signal can then be converted to the time domain using an IFFT, which is simply a sum of cosines with the same frequency as the delta functions. B. Picking a Channel 1) Using Distance Information: A protocol developed in [9] can be used to get an estimate on the distance between the transmitter and receiver. If this, or a similar protocol, is used then each transmitter should keep a cache of distance information to its one hop neighbors. If no distance is saved in cache then a greedy approach, described in the next section, can be used. If communication is established by this means the transmitter should initiate a ranging protocol for future data transfers. If the distance is cached then the transmitting node should use the highest possible frequency that it can use in order to reach the distance of the receiver. The frequency dependent attenuation has been characterize in [7] which can be used with the known distance to the receiver to estimate the absorption that the spectrum signal will see. Using the highest channel possible that reaches the receiver will ensure that the lower channels are left open for longer distance communication. Additionally the signal will get absorbed by the water more which reduces interference to further nodes allowing the chosen channel to be reused. 2) Greedy approach: If distance information is not known then a channel can be picked on a greedy basis. The channel that is least attenuated occurs at the lowest frequency. If a transmitter wants to ensure that a packet will be delivered with highest probability the lowest channel should be chosen, assuming equal noise among all channels. This is the best possible channel that the transmitter can pick without any other information about the distance to the receiver or the frequency spectrum at the receiver’s location. This does lead to inefficiency namely reduced network throughput. Both short and long links will try and use the lowest frequency when the short link does not necessarily need it. Therefore, the long link has to wait until the lower channels are free in order to begin a data transfer rather than multiple simultaneously transmissions that could have been possible if the distance information was
Fig. 3.
Transmitted two node spectrum signal
known. A scenario where this could be a problem is shown in figure 2. Channel 1 will be allocated to either node 1 or 2 depending on which node first transmits a spectrum signal. The range of channel 1 for nodes 1 and 2 is marked with a solid line while the range of a higher channel such as channel 3 is marked with a dotted line. Therefore if node 1 starts a transfer first then it will not be possible for node 2 to communicate with node 14 leading to a suboptimal channel assignment. C. Listening When a node is idle it is listening for possible spectrum signals. To do this an M point FFT can be used with coefficients ranging from X0 to XM −1 . In order to determine if a node is being signaled it examines the FFT points. This process is similar to that of the transmission but rather than placing the signal on the frequency channel, the receiver inspects the coefficients of the FFT. While listening, a node is constantly collecting samples and processing an FFT. The node will look at each Ci and check the coefficients that translate to the binary node ID. If all the coefficients that correspond to ones in the binary vector Ni are greater than a threshold tupper and all the coefficients that correspond to zero are less than a second threshold tlower , the node is being called to that channel. The node will then send back an acknowledgement to the transmitter on the chosen channel and the transfer will begin. D. Evaluation 1) Experimental: We have done some initial testing of the spectrum signaling protocol using GNU Radio, a software defined radio framework. GNU Radio uses flow graphs that are comprised of digital signal processing blocks that are linked together and samples are streamed between them. The Universal Software Radio Peripheral (USRP) is the hardware that we used for both analog to digital and digital to analog conversion. Using the low frequency daughterboards it is possible to sample from DC-30MHz however due to the limited bandwidth of the channel and the transducers we
Fig. 4.
Received two node spectrum signal
Fig. 5. Simulated collision percentage as a function of packet size with varying number of channels
never went above 200kHz. Most of the signal processing seen in this paper was done offline in Matlab although we are currently working on implementing everything in UANT [10], an underwater platform that uses GNU Radio for the physical layer. Figure 3 shows what a transmission with no loss in the channel would look like of two spectrum signals calling two different nodes simultaneously on different channels. We transmitted this signal through the water and the FFT snapshot seen at the receiver after collecting 1ms worth of samples can be seen in figure 4. On the channel that spans from 100 kHz to 110 kHz node ID 27 is being called with a center frequency of 105 kHz. Likewise on the channel with center frequency of 125 kHz node 14 is being called. A sample topology can be seen in figure 2. If both nodes 1 and 2 were signaling at the same time, this is similar to what would be seen by node 27. We tested sending 1000 spectrum signals through the water with a random receiving node ID Nj with 0 < j < 32 and
Fig. 6. Simulated collision percentage as a function of network size with varying number of channels
a random channel Ci where i = 8. We tried it both with and without a filter pump that will increase noise and introduce the Doppler effect. The detection algorithm, as described above, looks at each channel to try and find FFT coefficients above the threshold tupper or below tlower that could possibly map to a node ID. If there is a valid Nj in the channel it will be reported. We compared all detected spectrum signals with the ground truth of what was sent by the transmitter. The percentage of missed calls and false hops (average per node) can be seen in table I. A missed call denotes that a node was unable to hear a spectrum signal and did not hop to the channel. In this case the transmitter, after not receiving an acknowledgement, would transmit another spectrum signal. A false hop means that a node heard its node ID on the spectrum when it was not actually being called. This type of error is less serious since it can continue listening to spectrum signals, and once the node realizes there is no transfer it can hop to a new channel. 2) Simulation: We have simulated this protocol in Matlab to ensure scalability to a large network. In this simulation the channel model is simplified and the frequency dependence of the channel is not incorporated. Since the distance information in this case is not relevant the greedy approach is used. However, even with these simplifications the results from these simulations can still show the improvements of having an increased number of channels. In all simulations there is a random network topology in a 1 km3 3D space, with each nodes communication range 150 meters. For each figure shown we have averaged the results from 100 random topologies. In figure 5, we show that with increasing transmission packet size the resulting percentage of colliding packets increases. This is with a 60 node network with each node transmitting once a second at 100 B/s. Clearly an increased number of channels increases bandwidth, and with no FCC laws in place the full underwater acoustic spectrum can be used to take advantage of this. In figure 6, the size of the network is varied which increases
No Filter Pump With Filter Pump
Missed Calls 15.2% 17.0%
False Hops 1.42% 1.35%
TABLE I D ETECTION P ERFORMANCE
the node density while the packet size is held constant. Again increasing the number of channels dramatically reduces the number of collisions, increasing network throughput. IV. C ONSIDERATIONS A. Doppler Effect The unfriendly underwater channel can increase the chance of a possible missed detection when a transmitter is trying to call a receiver due to the Doppler effect. Underwater nodes can inherently move freely unless they are anchored down. Furthermore even if two nodes are stationary, the medium can move due to currents which can not always be neglected. This can effectively shift the Dirac delta functions (or modulated data) which were placed on the spectrum at the time of transmission. In order to cope with this unwanted behavior some precautions can be taken when listening for spectrum signals. When looking at the FFT coefficients that correspond to the listening nodes ID it is important to also look at all the neighboring coefficients that correspond to maximum possible Doppler shift. This maximum Doppler shift can be calculated, assuming the worst case ocean current and node movement. Furthermore the ∆f must be greater than the maximum possible Doppler shift otherwise it is possible that a one of the signaling δ(f − f0 ) has shifted into a new channel and the spectrum signal now spans two channels. B. Disambiguating data and spectrum signal In addition to the aforementioned precautions being taken to mitigate the Doppler effect, it is also important to be able to disambiguate between modulated data and a spectrum signal. A transmitting node will not call a receiver using a spectrum signal if it determines the channel is already occupied with a data transfer. However even if the transmitter determines that a channel is clear this might not be the case at the receiving node. Even if the receiving node is idle, it may be overhearing a transmission on another link while at the same time receiving a spectrum signal from another transmitter. While trying to distinguish if a node is actually being called to a channel, it is important to ensure that an overheard transmission is not mistaken for a bit in a spectrum signal. The technique to do this varies on the modulation scheme that is chosen to transmit data, we will point out a few techniques in the following sections. 1) MFSK: If MFSK is the modulation scheme that is used to transmit data, one simple solution is to throw out any node ID that is a power of two and not use it in the network. This will make every possible spectrum signal used to call a receiver have at least two peaks in the frequency domain. It is possible that a node overhears many simultaneous
transmissions at once. In this case two peaks that are from two different MFSK transmissions could correspond to a receivers node ID, even though it is not a spectrum signal. However this case is unlikely, since not only does a receiving node have to overhear two transmissions on the same channel at the same time, but it must also match the unique node ID of this receiver. To further reduce the probability of this error, it is possible to again remove all node IDs that have two binary ones in the node ID. 2) OFDM: The hardest modulation scheme to disambiguate from is OFDM. This is because OFDM with ASK as the subcarriers can make modulated data look identical to a spectrum signal. In this case again it is not possible to ensure that a node can be certain that it is being signaled rather than overhearing modulated data. However if lmax
